Software Engineering Challenges in the Deployment of Generative AI Models at Scale

Because generative AI has expanded quickly, today Large Language Models (LLMs) and diffusion-based generative models are transforming healthcare, financial, educational, and entertainment fields. Even though these models work well, getting them into use on a wide scale creates software engineering difficulties. Some of these issues include providing infrastructure, controlling inference latency, maintaining privacy, obeying ethics, building CI/CD processes, controlling versions, and keeping an eye on the models. The study explores the complicated aspects of putting generative AI models into real-world situations. In examining AI software engineering patterns, as well as its primary development and use processes, this study highlights the specific problems that still exist when employing traditional methods with AI systems. We share information about new methods and procedures made for AI operations (MLOps), highlight the value of joint efforts, and change the usual development steps. Next, the research paper demonstrates how a generative AI system was implemented in practice, points out problems that came up and reveals solutions, finishing with a guideline for proper and successful deployment.